library(ggplot2)
Assume we have yield (or other agronomic assessment) data in spatial format, such as yield monitor data. For example, from SDSU
raw.dat <- read.csv("./data/sdsu/Swest_Corn2013-Clean.csv", header = TRUE)
summary(raw.dat)
## ID OID Longitude Latitude
## Min. : 1 Min. : 1 Min. :-96.9 Min. :43.23
## 1st Qu.: 2905 1st Qu.: 8715 1st Qu.:-96.9 1st Qu.:43.23
## Median : 7054 Median :17428 Median :-96.9 Median :43.23
## Mean : 8303 Mean :17428 Mean :-96.9 Mean :43.23
## 3rd Qu.:12993 3rd Qu.:26142 3rd Qu.:-96.9 3rd Qu.:43.23
## Max. :21707 Max. :34856 Max. :-96.9 Max. :43.24
## Field Dataset Product Swath
## West_Field_SW:34856 Min. :-113892 0533HR:34856 Min. : 4.99
## 1st Qu.:-113892 1st Qu.:14.99
## Median :-113892 Median :14.99
## Mean :-113892 Mean :14.92
## 3rd Qu.:-113892 3rd Qu.:14.99
## Max. :-113892 Max. :14.99
## Distance Duration Track Elevation Areacount
## Min. :0.820 Min. :1 Min. : 0.0 Min. :1241 On:34856
## 1st Qu.:4.530 1st Qu.:1 1st Qu.: 0.0 1st Qu.:1252
## Median :4.950 Median :1 Median :148.3 Median :1255
## Mean :4.875 Mean :1 Mean :100.6 Mean :1255
## 3rd Qu.:5.250 3rd Qu.:1 3rd Qu.:180.0 3rd Qu.:1258
## Max. :7.190 Max. :1 Max. :359.5 Max. :1266
## Time Yoffset PassNum CropFlw
## 10/2/2013:34856 Min. :0 Min. : 1.00 Min. : 0.40
## 1st Qu.:0 1st Qu.:12.00 1st Qu.:18.10
## Median :0 Median :31.00 Median :20.60
## Mean :0 Mean :34.24 Mean :20.34
## 3rd Qu.:0 3rd Qu.:55.00 3rd Qu.:23.00
## Max. :0 Max. :73.00 Max. :35.60
## Moisture YldMassDry YldMassWet YldVolDry
## Min. : 0.00 Min. : 299.9 Min. : 307.1 Min. : 5.35
## 1st Qu.:16.87 1st Qu.:10866.3 1st Qu.:11173.1 1st Qu.:194.04
## Median :17.25 Median :11974.6 Median :12305.4 Median :213.83
## Mean :17.28 Mean :11837.6 Mean :12165.9 Mean :211.39
## 3rd Qu.:17.63 3rd Qu.:13059.2 3rd Qu.:13424.0 3rd Qu.:233.20
## Max. :24.00 Max. :22241.7 Max. :22736.6 Max. :397.17
## YldVolWet Speed Prod CropFlwV
## Min. : 5.48 Min. :0.560 Min. :1.020 Min. : 25.71
## 1st Qu.:199.52 1st Qu.:3.090 1st Qu.:5.570 1st Qu.:1163.57
## Median :219.74 Median :3.380 Median :6.140 Median :1324.28
## Mean :217.25 Mean :3.324 Mean :6.005 Mean :1307.62
## 3rd Qu.:239.71 3rd Qu.:3.580 3rd Qu.:6.460 3rd Qu.:1478.57
## Max. :406.01 Max. :4.900 Max. :8.900 Max. :2288.57
## Date
## 10/2/2013:34856
##
##
##
##
##
In this case, we’ll work with dry mass
hist(raw.dat$YldMassDry,main="YldVolDry")
ggplot(raw.dat, aes(Longitude, Latitude)) + geom_point(aes(colour = PassNum),size = 1)
We discard endrows for simplicity.
northBorder <- 43.23575
southBorder <- 43.22975
eastBorder <- -96.897
westBorder <- -96.900
min(raw.dat$Longitude)
## [1] -96.90052
max(raw.dat$Longitude)
## [1] -96.89669
trimmed.dat <- subset(raw.dat,raw.dat$Latitude>=southBorder)
trimmed.dat <- subset(trimmed.dat,trimmed.dat$Latitude<=northBorder)
trimmed.dat <- subset(trimmed.dat,trimmed.dat$Longitude<=eastBorder)
trimmed.dat <- subset(trimmed.dat,trimmed.dat$Longitude>=westBorder)
ggplot(trimmed.dat, aes(Longitude, Latitude)) + geom_point(aes(colour = YldVolDry),size = 1)
Now, let’s map a trial into one corner of the field. We will find this easier if we convert to meters. This will require an approximation, see http://stackoverflow.com/questions/639695/how-to-convert-latitude-or-longitude-to-meters
trimmed.dat$LonM <- trimmed.dat$Longitude - min(trimmed.dat$Longitude)
trimmed.dat$LatM <- trimmed.dat$Latitude - min(trimmed.dat$Latitude)
latMid <- (min(trimmed.dat$Latitude) + max(trimmed.dat$Latitude))/2
m_per_deg_lat = 111132.954 - 559.822 * cos( 2.0 * latMid ) + 1.175 * cos( 4.0 * latMid)
m_per_deg_lon = (3.14159265359/180 ) * 6367449 * cos ( latMid )
trimmed.dat$LonM <- trimmed.dat$LonM*m_per_deg_lon
trimmed.dat$LatM <- trimmed.dat$LatM*m_per_deg_lat
ggplot(trimmed.dat, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 1)
max(trimmed.dat$LonM)
## [1] 243.7949
max(trimmed.dat$LatM)
## [1] 666.539
So, let’s have a trial with plot dimensions of 2x4 m with 1 m buffer in between
plotDim <- c(2,4)
rowBuffer <- 1
We’ll start with Roy Scott’s 1993 plan
Scott.plan <- data.frame(
row=c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3),
col=c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
trt=c(1, 5, 7, 2, 6, 8, 3, 10, 4, 9, 11, 12, 3, 14, 13, 1, 16, 2, 15, 4, 1, 19, 1, 2, 21, 18, 3, 20, 4, 17)
)
trial.dimensions <- function(plan,plot.dim,row.buffer) {
rows <- max(plan$row)-min(plan$row)+1
cols <- max(plan$col)-min(plan$col)+1
plot.width <- plot.dim[1]*cols + (cols-1)*row.buffer
plot.height <- plot.dim[2]*rows + (rows-1)*row.buffer
return(c(plot.width,plot.height))
}
trial.dim <- trial.dimensions(Scott.plan,plotDim,rowBuffer)
Add 3 meters to each side and subset our yield data
trial.dat <- subset(trimmed.dat,trimmed.dat$LonM<(trial.dim[1]+6))
trial.dat <- subset(trial.dat,trial.dat$LatM<(trial.dim[2]+6))
ggplot(trial.dat, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 6)
Find points at the center of plots
Scott.plan$LonM <- 0
Scott.plan$LatM <- 0
start.point <- c(3,3)
half.width <- plotDim[1]/2
half.heigth <- plotDim[2]/2
for(idx in 1:dim(Scott.plan)[1]) {
row <- Scott.plan$row[idx]
col <- Scott.plan$col[idx]
Scott.plan$LonM[idx] <- start.point[1] + half.width + (col-1)*plotDim[1] + (col-1)*rowBuffer
Scott.plan$LatM[idx] <- start.point[2] + half.heigth + (row-1)*plotDim[2] + (row-1)*rowBuffer
}
ggplot(Scott.plan, aes(LonM, LatM)) + geom_point(aes(colour = row),size = 1)
Next, we krig the trial data
library(gstat)
sample.var <- variogram(YldVolDry~1,
locations=~LonM+LatM,
data=trimmed.dat)
plot(sample.var)
sample.vgm <- fit.variogram(sample.var, vgm("Exp"))
sample.vgm
## model psill range
## 1 Nug 577.3469 0.0000
## 2 Exp 192.0298 23.6442
sample.krig <- krige(id="YldVolDry",
formula=YldVolDry~1,
data = trial.dat,
newdata = Scott.plan,
model = sample.vgm,
locations=~LonM+LatM)
## [using ordinary kriging]
sample.krig
## LonM LatM YldVolDry.pred YldVolDry.var
## 1 4 5 219.7577 624.6749
## 2 7 5 220.4189 622.1267
## 3 10 5 222.1915 622.5545
## 4 13 5 223.7153 624.6057
## 5 16 5 225.1562 622.4185
## 6 19 5 225.2944 622.7788
## 7 22 5 223.2662 622.0513
## 8 25 5 218.5649 621.1611
## 9 28 5 213.9409 622.8205
## 10 31 5 208.6830 623.9615
## 11 4 10 220.6846 622.4553
## 12 7 10 222.9458 620.2207
## 13 10 10 222.8527 620.7529
## 14 13 10 222.6355 622.1328
## 15 16 10 222.2083 620.1043
## 16 19 10 220.3901 621.0249
## 17 22 10 218.8548 619.9384
## 18 25 10 217.7635 619.5662
## 19 28 10 214.1294 620.8849
## 20 31 10 209.2803 621.3399
## 21 4 15 225.6756 624.3709
## 22 7 15 226.7191 622.4411
## 23 10 15 225.5107 622.8796
## 24 13 15 222.8781 624.1540
## 25 16 15 219.9744 621.7598
## 26 19 15 218.3965 622.3589
## 27 22 15 216.9719 622.1420
## 28 25 15 215.4486 621.5204
## 29 28 15 212.9803 622.8373
## 30 31 15 208.9109 623.9846
Scott.plan$YldVolDry <- sample.krig$YldVolDry.pred
ggplot(Scott.plan, aes(LonM, LatM)) + geom_point(aes(colour = row),size = 4)
pooled.dat <- data.frame(
LonM = c(trial.dat$LonM,Scott.plan$LonM),
LatM = c(trial.dat$LatM,Scott.plan$LatM),
YldVolDry = c(trial.dat$YldVolDry,Scott.plan$YldVolDry)
)
ggplot(pooled.dat, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 4)
library(lme4)
## Loading required package: Matrix
summary(aov(YldVolDry ~ trt + row, data=Scott.plan))
## Df Sum Sq Mean Sq F value Pr(>F)
## trt 1 19.0 19.044 0.703 0.409
## row 1 0.1 0.086 0.003 0.955
## Residuals 27 731.2 27.080
gy.lmer <- lmer(YldVolDry ~ trt + (1 | row), data=Scott.plan)
anova(gy.lmer)
## Analysis of Variance Table
## Df Sum Sq Mean Sq F value
## trt 1 19.044 19.044 0.7292
print(gy.lmer, corr=FALSE)
## Linear mixed model fit by REML ['lmerMod']
## Formula: YldVolDry ~ trt + (1 | row)
## Data: Scott.plan
## REML criterion at convergence: 181.3479
## Random effects:
## Groups Name Std.Dev.
## row (Intercept) 0.00
## Residual 5.11
## Number of obs: 30, groups: row, 3
## Fixed Effects:
## (Intercept) trt
## 220.5747 -0.1232
aug16.plan <- data.frame(
row=c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8),
col=c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16)
)
Create a function to overlay a plan on a yield map
superimpose.plan <- function(plan,map.data,start.point,plot.dim=c(1,1),row.buffer=0,sample.vgm=NULL) {
trial.dim <- trial.dimensions(plan,plotDim,row.buffer)
trial.dat <- subset(map.data,map.data$LonM<(start.point[1]+trial.dim[1]+3))
trial.dat <- subset(trial.dat,trial.dat$LatM<(start.point[2]+trial.dim[2]+3))
trial.dat <- subset(trial.dat,trial.dat$LonM>=(start.point[1]-3))
trial.dat <- subset(trial.dat,trial.dat$LatM>=(start.point[2]-3))
plan$LonM <- 0
plan$LatM <- 0
half.width <- plot.dim[1]/2
half.heigth <- plot.dim[2]/2
for(idx in 1:dim(plan)[1]) {
row <- plan$row[idx]
col <- plan$col[idx]
plan$LonM[idx] <- start.point[1] + half.width + (col-1)*plot.dim[1] + (col-1)*row.buffer
plan$LatM[idx] <- start.point[2] + half.heigth + (row-1)*plot.dim[2] + (row-1)*row.buffer
}
if(is.null(sample.vgm)) {
sample.var <- variogram(YldVolDry~1,
locations=~LonM+LatM,
data=map.data)
sample.vgm <- fit.variogram(sample.var, vgm("Exp"))
}
sample.krig <- krige(id="YldVolDry",
formula=YldVolDry~1,
data = trial.dat,
newdata = plan,
model = sample.vgm,
locations=~LonM+LatM)
plan$YldVolDry <- sample.krig$YldVolDry.pred
return(list(
trial.dim=trial.dim,
plan=plan,
trial=trial.dat,
krig=sample.krig,
pooled = data.frame(
LonM = c(trial.dat$LonM,plan$LonM),
LatM = c(trial.dat$LatM,plan$LatM),
YldVolDry = c(trial.dat$YldVolDry,plan$YldVolDry)
)
))
}
plan16sw <- superimpose.plan(aug16.plan,
map.data=trimmed.dat,
start.point=c(3,3),
plot.dim=c(2,4),
row.buffer=1,sample.vgm=sample.vgm
)
## [using ordinary kriging]
ggplot(plan16sw$trial, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 6)
ggplot(plan16sw$plan, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 4)
ggplot(plan16sw$pooled, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 4)
plan16sw$trial.dim
## [1] 47 39
#plan16sw$plan
plan16se <- superimpose.plan(aug16.plan,
map.data=trimmed.dat,
start.point=c(190,3),
plot.dim=c(2,4),
row.buffer=1,sample.vgm=sample.vgm
)
## [using ordinary kriging]
ggplot(plan16se$trial, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 6)
ggplot(plan16se$plan, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 4)
ggplot(plan16se$pooled, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 4)
#plan16se$plan
plan16nw <- superimpose.plan(aug16.plan,
map.data=trimmed.dat,
start.point=c(3,621),
plot.dim=c(2,4),
row.buffer=1,sample.vgm=sample.vgm
)
## [using ordinary kriging]
ggplot(plan16nw$trial, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 6)
ggplot(plan16nw$plan, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 4)
ggplot(plan16nw$pooled, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 4)
#plan16nw$plan
plan16ne <- superimpose.plan(aug16.plan,
map.data=trimmed.dat,
start.point=c(190,621),
plot.dim=c(2,4),
row.buffer=1,sample.vgm=sample.vgm
)
## [using ordinary kriging]
ggplot(plan16ne$trial, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 6)
ggplot(plan16ne$plan, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 4)
ggplot(plan16ne$pooled, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 4)
#plan16ne$plan
aug8.plan <- data.frame(
row=c(1,1,1,1,1,1,1,1,
2,2,2,2,2,2,2,2,
3,3,3,3,3,3,3,3,
4,4,4,4,4,4,4,4,
5,5,5,5,5,5,5,5,
6,6,6,6,6,6,6,6,
7,7,7,7,7,7,7,7,
8,8,8,8,8,8,8,8,
9,9,9,9,9,9,9,9,
10,10,10,10,10,10,10,10,
11,11,11,11,11,11,11,11,
12,12,12,12,12,12,12,12,
13,13,13,13,13,13,13,13,
14,14,14,14,14,14,14,14,
15,15,15,15,15,15,15,15,
16,16,16,16,16,16,16,16),
col=c(1,2,3,4,5,6,7,8,
8,7,6,5,4,3,2,1,
1,2,3,4,5,6,7,8,
8,7,6,5,4,3,2,1,
1,2,3,4,5,6,7,8,
8,7,6,5,4,3,2,1,
1,2,3,4,5,6,7,8,
8,7,6,5,4,3,2,1,
1,2,3,4,5,6,7,8,
8,7,6,5,4,3,2,1,
1,2,3,4,5,6,7,8,
8,7,6,5,4,3,2,1,
1,2,3,4,5,6,7,8,
8,7,6,5,4,3,2,1,
1,2,3,4,5,6,7,8,
8,7,6,5,4,3,2,1)
)
plan8sw <- superimpose.plan(aug8.plan,
map.data=trimmed.dat,
start.point=c(3,3),
plot.dim=c(2,4),
row.buffer=1,sample.vgm=sample.vgm
)
## [using ordinary kriging]
ggplot(plan8sw$trial, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 6)
ggplot(plan8sw$plan, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 4)
ggplot(plan8sw$pooled, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 4)
plan8sw$trial.dim
## [1] 23 79
#plan8sw$plan
plan8se <- superimpose.plan(aug8.plan,
map.data=trimmed.dat,
start.point=c(218,3),
plot.dim=c(2,4),
row.buffer=1,sample.vgm=sample.vgm
)
## [using ordinary kriging]
ggplot(plan8se$trial, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 6)
ggplot(plan8se$plan, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 4)
ggplot(plan8se$pooled, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 4)
plan8se$trial.dim
## [1] 23 79
#plan8se$plan
plan8nw <- superimpose.plan(aug8.plan,
map.data=trimmed.dat,
start.point=c(3,581),
plot.dim=c(2,4),
row.buffer=1,sample.vgm=sample.vgm
)
## [using ordinary kriging]
ggplot(plan8nw$trial, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 6)
ggplot(plan8nw$plan, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 4)
ggplot(plan8nw$pooled, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 4)
plan8nw$trial.dim
## [1] 23 79
#plan8nw$plan
plan8ne <- superimpose.plan(aug8.plan,
map.data=trimmed.dat,
start.point=c(218,581),
plot.dim=c(2,4),
row.buffer=1,sample.vgm=sample.vgm
)
## [using ordinary kriging]
ggplot(plan8ne$trial, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 6)
ggplot(plan8ne$plan, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 4)
ggplot(plan8ne$pooled, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 4)
plan8ne$trial.dim
## [1] 23 79
#plan8ne$plan
aug4.plan <- data.frame(
row=c(1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5,6,6,6,6,7,7,7,7,8,8,8,8,9,9,9,9,10,10,10,10,11,11,11,11,12,12,12,12,13,13,13,13,14,14,14,14,15,15,15,15,16,16,16,16,17,17,17,17,18,18,18,18,19,19,19,19,20,20,20,20,21,21,21,21,22,22,22,22,23,23,23,23,24,24,24,24,25,25,25,25,26,26,26,26,27,27,27,27,28,28,28,28,29,29,29,29,30,30,30,30,31,31,31,31,32,32,32,32),
col=c(1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4)
)
plan4 <- superimpose.plan(aug4.plan,
map.data=trimmed.dat,
start.point=c(3,3),
plot.dim=c(2,4),
row.buffer=1,sample.vgm=sample.vgm
)
## [using ordinary kriging]
ggplot(plan4$trial, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 6)
ggplot(plan4$plan, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 4)
ggplot(plan4$pooled, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 4)
#plan4$plan
Spatially balanced plans
plan8x14.plan <- data.frame(
row=c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,5,5,5,5,5,5,5,5,5,5,5,5,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,7,7,7,7,7,7,7,7,7,7,7,7,7,7,8,8,8,8,8,8,8,8,8,8,8,8,8,8),
col=c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,1,2,3,4,5,6,7,8,9,10,11,12,13,14,1,2,3,4,5,6,7,8,9,10,11,12,13,14,1,2,3,4,5,6,7,8,9,10,11,12,13,14,1,2,3,4,5,6,7,8,9,10,11,12,13,14,1,2,3,4,5,6,7,8,9,10,11,12,13,14,1,2,3,4,5,6,7,8,9,10,11,12,13,14,1,2,3,4,5,6,7,8,9,10,11,12,13,14),
vanEs14x8=c(1,12,14,6,5,8,2,7,10,11,13,4,9,3,5,7,1,9,8,4,11,13,6,12,10,2,3,14,7,10,11,14,9,1,6,12,13,3,4,5,2,8,6,4,10,1,3,5,8,14,11,13,9,12,7,2,11,8,3,12,1,7,4,6,2,9,14,10,13,5,13,1,2,11,4,14,7,3,5,6,12,9,8,10,12,11,5,4,10,2,1,9,14,8,3,7,13,6,4,14,8,7,13,12,10,1,3,5,2,11,6,9)
)
plan8x14.plan$blk <- plan8x14.plan$row
plan8x14.plan$trt <- plan8x14.plan$vanEs14x8
plan8x14sw <- superimpose.plan(plan8x14.plan,
map.data=trimmed.dat,
start.point=c(3,3),
plot.dim=c(2,4),
row.buffer=1,sample.vgm=sample.vgm
)
## [using ordinary kriging]
ggplot(plan8x14sw$trial, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 6)
ggplot(plan8x14sw$plan, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 4)
ggplot(plan8x14sw$pooled, aes(LonM, LatM)) + geom_point(aes(colour = YldVolDry),size = 4)
aov.tbl <- summary(aov(YldVolDry ~ as.factor(trt)+as.factor(blk),data=plan8x14sw$plan))
aov.tbl
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(trt) 13 574 44.1 0.899 0.557
## as.factor(blk) 7 3663 523.3 10.663 9.81e-10 ***
## Residuals 91 4466 49.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
trial.dim <- trial.dimensions(plan8x14.plan,plot.dim=c(2,4),row.buffer=1)
trial.width <- trial.dim[1]
trial.height <- trial.dim[2]
TrtMS <- c()
TrtP <- c()
RepMS <- c()
RepP <- c()
ResMS <- c()
ResP <- c()
Row <- c()
Col <- c()
rightBorder <- max(trimmed.dat$LonM) - (trial.width+6)
topBorder <- max(trimmed.dat$LatM) - (trial.height+6)
row=1
col=1
atColEnd=FALSE
atRowEnd=FALSE
while(!atColEnd) {
currentRow <- 3+(row-1)*trial.height + (row-1)*6
if(currentRow < topBorder) {
while(!atRowEnd) {
currentCol <- 3+(col-1)*trial.width + (col-1)*6
if(currentCol<rightBorder) {
corner <- c(currentCol,currentRow)
print(corner)
#overlay and analyze
currentPlan <- superimpose.plan(plan8x14.plan,
map.data=trimmed.dat,
start.point=corner,
plot.dim=c(2,4),
row.buffer=1,sample.vgm=sample.vgm
)
aov.tbl <- summary(aov(YldVolDry ~ as.factor(vanEs14x8)+as.factor(row),data=currentPlan$plan))
TrtMS <- c(TrtMS,aov.tbl[[1]][1,3])
TrtP <- c(TrtP,aov.tbl[[1]][1,5])
RepMS <- c(RepMS,aov.tbl[[1]][2,3])
RepP <- c(RepP,aov.tbl[[1]][2,5])
ResMS <- c(ResMS,aov.tbl[[1]][3,3])
ResP <- c(ResP,aov.tbl[[1]][3,5])
Row <- c(Row,corner[1])
Col <- c(Col,corner[2])
col=col+1
} else {
atRowEnd=TRUE
}
}
col=1
row=row+1
atRowEnd=FALSE
} else {
atColEnd =TRUE
}
}
## [1] 3 3
## [using ordinary kriging]
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## [using ordinary kriging]
hist(TrtMS)
hist(TrtP)
hist(RepMS)
hist(RepP)
hist(ResMS)
sim.dat <- data.frame(
TrtMS = TrtMS,
TrtP = TrtP,
RepMS = RepMS,
RepP = RepP,
ResMS = ResMS,
Row = Row,
Col = Col
)
ggplot(sim.dat, aes(Row, Col)) + geom_point(aes(colour = RepP),size = 8)
ggplot(sim.dat, aes(Row, Col)) + geom_point(aes(colour = ResMS),size = 8)
ggplot(sim.dat, aes(Row, Col)) + geom_point(aes(colour = TrtP),size = 8)
And a function to simulate a plan over a field
simulate.plan <- function(plan,field,plot.dim=c(1,1),row.buffer=0,sample.vgm=NULL) {
trial.dim <- trial.dimensions(plan,plot.dim=plot.dim,row.buffer=row.buffer)
trial.width <- trial.dim[1]
trial.height <- trial.dim[2]
if(is.null(sample.vgm)) {
sample.var <- variogram(YldVolDry~1,
locations=~LonM+LatM,
data=map.data)
sample.vgm <- fit.variogram(sample.var, vgm("Exp"))
}
TrtMS <- c()
TrtDF <- c()
TrtP <- c()
RepMS <- c()
RepDF <- c()
RepP <- c()
ResMS <- c()
ResP <- c()
ResDF <- c()
Row <- c()
Col <- c()
rightBorder <- max(field$LonM) - (trial.width+6)
topBorder <- max(field$LatM) - (trial.height+6)
row=1
col=1
atColEnd=FALSE
atRowEnd=FALSE
while(!atColEnd) {
currentRow <- 3+(row-1)*trial.height + (row-1)*6
if(currentRow < topBorder) {
while(!atRowEnd) {
currentCol <- 3+(col-1)*trial.width + (col-1)*6
if(currentCol<rightBorder) {
corner <- c(currentCol,currentRow)
#overlay and analyze
currentPlan <- superimpose.plan(plan,
map.data=field,
start.point=corner,
plot.dim=c(2,4),
row.buffer=1,sample.vgm=sample.vgm
)
aov.tbl <- summary(aov(YldVolDry ~ as.factor(trt)+as.factor(blk),data=currentPlan$plan))
TrtDF <- c(TrtDF,aov.tbl[[1]][1,1])
TrtMS <- c(TrtMS,aov.tbl[[1]][1,3])
TrtP <- c(TrtP,aov.tbl[[1]][1,5])
RepDF <- c(RepDF,aov.tbl[[1]][2,1])
RepMS <- c(RepMS,aov.tbl[[1]][2,3])
RepP <- c(RepP,aov.tbl[[1]][2,5])
ResDF <- c(ResDF,aov.tbl[[1]][3,1])
ResMS <- c(ResMS,aov.tbl[[1]][3,3])
ResP <- c(ResP,aov.tbl[[1]][3,5])
Row <- c(Row,corner[1])
Col <- c(Col,corner[2])
col=col+1
} else {
atRowEnd=TRUE
}
}
col=1
row=row+1
atRowEnd=FALSE
} else {
atColEnd =TRUE
}
}
sim.dat <- data.frame(
TrtDF = TrtDF,
TrtMS = TrtMS,
TrtP = TrtP,
RepDF = RepDF,
RepMS = RepMS,
RepP = RepP,
ResDF = ResDF,
ResMS = ResMS,
Row = Row,
Col = Col
)
return(sim.dat)
}
Stagger 0
aug14.plan <- data.frame(
row=c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,5,5,5,5,5,5,5,5,5,5,5,5,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,7,7,7,7,7,7,7,7,7,7,7,7,7,7,8,8,8,8,8,8,8,8,8,8,8,8,8,8),
col=c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,1,2,3,4,5,6,7,8,9,10,11,12,13,14,1,2,3,4,5,6,7,8,9,10,11,12,13,14,1,2,3,4,5,6,7,8,9,10,11,12,13,14,1,2,3,4,5,6,7,8,9,10,11,12,13,14,1,2,3,4,5,6,7,8,9,10,11,12,13,14,1,2,3,4,5,6,7,8,9,10,11,12,13,14,1,2,3,4,5,6,7,8,9,10,11,12,13,14),
trt=c(81,1,2,3,82,4,5,6,83,7,8,9,84,10,84,11,12,13,81,14,15,16,83,17,18,19,82,20,83,21,22,23,81,24,25,26,82,27,28,29,84,30,82,31,32,33,81,34,35,36,84,37,38,39,83,40,83,41,42,43,84,44,45,46,81,47,48,49,82,50,84,51,52,53,83,54,55,56,82,57,58,59,81,60,81,61,62,63,83,64,65,66,84,67,68,69,82,70,81,71,72,73,82,74,75,76,84,77,78,79,83,80)
)
aug14.plan$blk <- aug14.plan$row
trial.dimensions(aug14.plan,plot.dim=c(2,4),row.buffer=1)
## [1] 41 39
stag0 <- simulate.plan(aug14.plan,trimmed.dat,plot.dim=c(2,4),row.buffer=1,sample.vgm=sample.vgm)
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
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## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
ggplot(stag0, aes(Row, Col)) + geom_point(aes(colour = RepP),size = 8)
ggplot(stag0, aes(Row, Col)) + geom_point(aes(colour = ResMS),size = 8)
ggplot(stag0, aes(Row, Col)) + geom_point(aes(colour = TrtP),size = 8)
Stagger 1
aug14.plan$trt <- c(81,1,2,3,82,4,5,6,83,7,8,9,84,10,20,83,11,12,13,84,14,15,16,81,17,18,19,82,83,30,84,21,22,23,81,24,25,26,82,27,28,29,39,81,40,83,31,32,33,82,34,35,36,84,37,38,48,49,82,50,84,41,42,43,83,44,45,46,81,47,57,58,59,84,60,83,51,52,53,81,54,55,56,82,83,67,68,69,81,70,84,61,62,63,82,64,65,66,76,81,77,78,79,84,80,82,71,72,73,83,74,75)
stag1 <- simulate.plan(aug14.plan,trimmed.dat,plot.dim=c(2,4),row.buffer=1,sample.vgm=sample.vgm)
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
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## [using ordinary kriging]
ggplot(stag1, aes(Row, Col)) + geom_point(aes(colour = RepP),size = 8)
ggplot(stag1, aes(Row, Col)) + geom_point(aes(colour = ResMS),size = 8)
ggplot(stag1, aes(Row, Col)) + geom_point(aes(colour = TrtP),size = 8)
Stagger 2
aug14.plan$trt <- c(81,1,2,3,82,4,5,6,83,7,8,9,84,10,83,20,84,11,12,13,81,14,15,16,82,17,18,19,28,29,84,30,81,21,22,23,82,24,25,26,83,27,84,37,38,39,81,40,82,31,32,33,83,34,35,36,45,46,81,47,48,49,82,50,83,41,42,43,84,44,83,54,55,56,82,57,58,59,81,60,84,51,52,53,62,63,81,64,65,66,84,67,68,69,83,70,82,61,83,71,72,73,84,74,75,76,82,77,78,79,81,80)
stag2 <- simulate.plan(aug14.plan,trimmed.dat,plot.dim=c(2,4),row.buffer=1,sample.vgm=sample.vgm)
## [using ordinary kriging]
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ggplot(stag2, aes(Row, Col)) + geom_point(aes(colour = RepP),size = 8)
ggplot(stag2, aes(Row, Col)) + geom_point(aes(colour = ResMS),size = 8)
ggplot(stag2, aes(Row, Col)) + geom_point(aes(colour = TrtP),size = 8)
aug14_7.plan <- data.frame(
row=c(1,1,1,1,1,1,1,2,2,2,2,2,2,2,3,3,3,3,3,3,3,4,4,4,4,4,4,4,5,5,5,5,5,5,5,6,6,6,6,6,6,6,7,7,7,7,7,7,7,8,8,8,8,8,8,8,9,9,9,9,9,9,9,10,10,10,10,10,10,10,11,11,11,11,11,11,11,12,12,12,12,12,12,12,13,13,13,13,13,13,13,14,14,14,14,14,14,14,15,15,15,15,15,15,15,16,16,16,16,16,16,16),
col=c(1,2,3,4,5,6,7,1,2,3,4,5,6,7,1,2,3,4,5,6,7,1,2,3,4,5,6,7,1,2,3,4,5,6,7,1,2,3,4,5,6,7,1,2,3,4,5,6,7,1,2,3,4,5,6,7,1,2,3,4,5,6,7,1,2,3,4,5,6,7,1,2,3,4,5,6,7,1,2,3,4,5,6,7,1,2,3,4,5,6,7,1,2,3,4,5,6,7,1,2,3,4,5,6,7,1,2,3,4,5,6,7),
trt=c(81,1,2,3,82,4,5,83,6,7,8,84,9,10,81,11,12,13,84,14,15,82,16,17,18,83,19,20,82,21,22,23,84,24,25,83,26,27,28,81,29,30,83,31,32,33,81,34,35,84,36,37,38,82,39,40,81,41,42,43,82,44,45,84,46,47,48,83,49,50,82,51,52,53,84,54,55,83,56,57,58,81,59,60,82,61,62,63,81,64,65,84,66,67,68,83,69,70,83,71,72,73,82,74,75,84,76,77,78,81,79,80)
)
aug14_7.plan$blk <- ceiling(aug14_7.plan$row/2)
stag0b <- simulate.plan(aug14_7.plan,trimmed.dat,plot.dim=c(2,4),row.buffer=1,sample.vgm=sample.vgm)
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
ggplot(stag0b, aes(Row, Col)) + geom_point(aes(colour = RepP),size = 8)
ggplot(stag0b, aes(Row, Col)) + geom_point(aes(colour = ResMS),size = 8)
ggplot(stag0b, aes(Row, Col)) + geom_point(aes(colour = TrtP),size = 8)
aug14_7.plan$trt <- c(81,1,2,3,82,4,5,10,83,6,7,8,84,9,14,15,82,11,12,13,83,81,19,20,84,16,17,18,23,82,24,25,83,21,22,27,28,84,29,30,81,26,31,32,33,81,34,35,82,83,36,37,38,84,39,40,45,82,41,42,43,81,44,49,50,84,46,47,48,83,82,54,55,83,51,52,53,58,84,59,60,81,56,57,62,63,81,64,65,82,61,66,67,68,84,69,70,83,84,71,72,73,83,74,75,80,82,76,77,78,81,79)
stag1b <- simulate.plan(aug14_7.plan,trimmed.dat,plot.dim=c(2,4),row.buffer=1,sample.vgm=sample.vgm)
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
ggplot(stag1b, aes(Row, Col)) + geom_point(aes(colour = RepP),size = 8)
ggplot(stag1b, aes(Row, Col)) + geom_point(aes(colour = ResMS),size = 8)
ggplot(stag1b, aes(Row, Col)) + geom_point(aes(colour = TrtP),size = 8)
aug14_7.plan$trt <- c(81,1,2,3,82,4,5,9,10,83,6,7,8,84,13,81,14,15,82,11,12,16,17,18,83,19,20,84,25,82,21,22,23,84,24,83,29,30,81,26,27,28,32,33,81,34,35,82,31,84,36,37,38,83,39,40,44,45,83,41,42,43,81,48,84,49,50,82,46,47,51,52,53,82,54,55,81,60,83,56,57,58,84,59,83,64,65,84,61,62,63,67,68,81,69,70,82,66,84,71,72,73,82,74,75,79,80,81,76,77,78,83)
stag2b <- simulate.plan(aug14_7.plan,trimmed.dat,plot.dim=c(2,4),row.buffer=1,sample.vgm=sample.vgm)
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
ggplot(stag2b, aes(Row, Col)) + geom_point(aes(colour = RepP),size = 8)
ggplot(stag2b, aes(Row, Col)) + geom_point(aes(colour = ResMS),size = 8)
ggplot(stag2b, aes(Row, Col)) + geom_point(aes(colour = TrtP),size = 8)
stag0$stagger <- "14.0"
stag1$stagger <- "14.1"
stag2$stagger <- "14.2"
stag0b$stagger <- "7.0"
stag1b$stagger <- "7.1"
stag2b$stagger <- "7.2"
stag.dat <- rbind(stag0,stag1,stag2,stag0b,stag1b,stag2b)
stag.dat$stagger <- as.factor(stag.dat$stagger)
summary(aov(TrtMS ~ stagger,data=stag.dat))
## Df Sum Sq Mean Sq F value Pr(>F)
## stagger 5 13448 2690 0.574 0.72
## Residuals 393 1840492 4683
plot(TrtMS ~ stagger,data=stag.dat)
summary(aov(TrtP ~ stagger,data=stag.dat))
## Df Sum Sq Mean Sq F value Pr(>F)
## stagger 5 14.10 2.8205 43.62 <2e-16 ***
## Residuals 393 25.41 0.0647
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(TrtP ~ stagger,data=stag.dat)
summary(aov(RepMS ~ stagger,data=stag.dat))
## Df Sum Sq Mean Sq F value Pr(>F)
## stagger 5 2964297 592859 10.66 1.27e-09 ***
## Residuals 393 21853553 55607
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(RepMS ~ stagger,data=stag.dat)
summary(aov(RepP ~ stagger,data=stag.dat))
## Df Sum Sq Mean Sq F value Pr(>F)
## stagger 5 16.56 3.312 39.48 <2e-16 ***
## Residuals 393 32.97 0.084
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(RepP ~ stagger,data=stag.dat)
summary(aov(ResMS ~ stagger,data=stag.dat))
## Df Sum Sq Mean Sq F value Pr(>F)
## stagger 5 128074 25615 8.25 2e-07 ***
## Residuals 393 1220227 3105
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(ResMS ~ stagger,data=stag.dat)
plot(TrtDF ~ stagger,data=stag.dat)
plot(RepDF ~ stagger,data=stag.dat)
plan16x7.plan <- plan8x14.plan
plan16x7.plan$row=aug14_7.plan$row
plan16x7.plan$col=aug14_7.plan$col
plan16x7.plan$blk <- ceiling(plan16x7.plan$row/2)
stag.vanes7 <- simulate.plan(plan16x7.plan,trimmed.dat,plot.dim=c(2,4),row.buffer=1,sample.vgm=sample.vgm)
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
ggplot(stag.vanes7, aes(Row, Col)) + geom_point(aes(colour = RepP),size = 8)
ggplot(stag.vanes7, aes(Row, Col)) + geom_point(aes(colour = ResMS),size = 8)
ggplot(stag.vanes7, aes(Row, Col)) + geom_point(aes(colour = TrtP),size = 8)
hist(stag.vanes7$TrtMS)
hist(stag.vanes7$TrtP)
hist(stag.vanes7$RepMS)
hist(stag.vanes7$RepP)
hist(stag.vanes7$ResMS)
max(stag0$Row)
## [1] 191
max(stag0$Col)
## [1] 588
max(stag0b$Row)
## [1] 211
max(stag0b$Col)
## [1] 513
8 rows by 14 columns vanEs14x8 aug80v140n, etc.
max(stag.vanes7$Row)
## [1] 211
max(stag.vanes7$Col)
## [1] 513
plan8x14sw <- superimpose.plan(plan8x14.plan,
map.data=trimmed.dat,
start.point=c(3,3),
plot.dim=c(2,4),
row.buffer=1,sample.vgm=sample.vgm
)
## [using ordinary kriging]
#plan8x14sw$plan
plan8x14se <- superimpose.plan(plan8x14.plan,
map.data=trimmed.dat,
start.point=c(211,513),
plot.dim=c(2,4),
row.buffer=1,sample.vgm=sample.vgm
)
## [using ordinary kriging]
#plan8x14se$plan
plan8x14nw <- superimpose.plan(plan8x14.plan,
map.data=trimmed.dat,
start.point=c(3,3),
plot.dim=c(2,4),
row.buffer=1,sample.vgm=sample.vgm
)
## [using ordinary kriging]
#plan8x14nw$plan
plan8x14ne <- superimpose.plan(plan8x14.plan,
map.data=trimmed.dat,
start.point=c(211,513),
plot.dim=c(2,4),
row.buffer=1,sample.vgm=sample.vgm
)
## [using ordinary kriging]
#plan8x14ne$plan
stag.vanes7 <- simulate.plan(plan16x7.plan,trimmed.dat,plot.dim=c(2,4),row.buffer=1,sample.vgm=sample.vgm)
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
max(stag.vanes7$Row)
## [1] 211
max(stag.vanes7$Col)
## [1] 513
16 rows by 7 columns
aug80v70n, etc.
plan16x7sw <- superimpose.plan(plan16x7.plan,
map.data=trimmed.dat,
start.point=c(3,3),
plot.dim=c(2,4),
row.buffer=1,sample.vgm=sample.vgm
)
## [using ordinary kriging]
#plan16x7sw$plan
plan16x7nw <- superimpose.plan(plan16x7.plan,
map.data=trimmed.dat,
start.point=c(211,3),
plot.dim=c(2,4),
row.buffer=1,sample.vgm=sample.vgm
)
## [using ordinary kriging]
#plan16x7nw$plan
plan16x7se <- superimpose.plan(plan16x7.plan,
map.data=trimmed.dat,
start.point=c(3,513),
plot.dim=c(2,4),
row.buffer=1,sample.vgm=sample.vgm
)
## [using ordinary kriging]
#plan16x7se$plan
plan16x7ne <- superimpose.plan(plan16x7.plan,
map.data=trimmed.dat,
start.point=c(211,513),
plot.dim=c(2,4),
row.buffer=1,sample.vgm=sample.vgm
)
## [using ordinary kriging]
#plan16x7ne$plan
plan32x4.plan <- data.frame(
row=c(1,1,1,1,2,2,2,2,3,3,3,3,4,4,5,5,5,5,6,6,6,6,7,7,7,7,8,8,9,9,9,9,10,10,10,10,11,11,11,11,12,12,13,13,13,13,14,14,14,14,15,15,15,15,16,16,17,17,17,17,18,18,18,18,19,19,19,19,20,20,21,21,21,21,22,22,22,22,23,23,23,23,24,24,25,25,25,25,26,26,26,26,27,27,27,27,28,28,29,29,29,29,30,30,30,30,31,31,31,31,32,32),
col=c(1,2,3,4,1,2,3,4,1,2,3,4,1,2,1,2,3,4,1,2,3,4,1,2,3,4,1,2,1,2,3,4,1,2,3,4,1,2,3,4,1,2,1,2,3,4,1,2,3,4,1,2,3,4,1,2,1,2,3,4,1,2,3,4,1,2,3,4,1,2,1,2,3,4,1,2,3,4,1,2,3,4,1,2,1,2,3,4,1,2,3,4,1,2,3,4,1,2,1,2,3,4,1,2,3,4,1,2,3,4,1,2),
trt=c(1,12,14,6,5,8,2,7,10,11,13,4,9,3,5,7,1,9,8,4,11,13,6,12,10,2,3,14,7,10,11,14,9,1,6,12,13,3,4,5,2,8,6,4,10,1,3,5,8,14,11,13,9,12,7,2,11,8,3,12,1,7,4,6,2,9,14,10,13,5,13,1,2,11,4,14,7,3,5,6,12,9,8,10,12,11,5,4,10,2,1,9,14,8,3,7,13,6,4,14,8,7,13,12,10,1,3,5,2,11,9,6))
plan32x4.plan$blk <- ceiling(plan32x4.plan$row/4)
stag.vanes4 <- simulate.plan(plan32x4.plan,trimmed.dat,plot.dim=c(2,4),row.buffer=1,sample.vgm=sample.vgm)
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
## [using ordinary kriging]
ggplot(stag.vanes4, aes(Row, Col)) + geom_point(aes(colour = RepP),size = 8)
ggplot(stag.vanes4, aes(Row, Col)) + geom_point(aes(colour = ResMS),size = 8)
ggplot(stag.vanes4, aes(Row, Col)) + geom_point(aes(colour = TrtP),size = 8)
hist(stag.vanes4$TrtMS)
hist(stag.vanes4$TrtP)
hist(stag.vanes4$RepMS)
hist(stag.vanes4$RepP)
hist(stag.vanes4$ResMS)
max(stag.vanes4$Row)
## [1] 224
max(stag.vanes4$Col)
## [1] 498
plan32x4sw <- superimpose.plan(plan32x4.plan,
map.data=trimmed.dat,
start.point=c(3,3),
plot.dim=c(2,4),
row.buffer=1,sample.vgm=sample.vgm
)
## [using ordinary kriging]
#plan32x4sw$plan
plan32x4nw <- superimpose.plan(plan32x4.plan,
map.data=trimmed.dat,
start.point=c(3,498),
plot.dim=c(2,4),
row.buffer=1,sample.vgm=sample.vgm
)
## [using ordinary kriging]
#plan32x4nw$plan
plan32x4ne <- superimpose.plan(plan32x4.plan,
map.data=trimmed.dat,
start.point=c(224,498),
plot.dim=c(2,4),
row.buffer=1,sample.vgm=sample.vgm
)
## [using ordinary kriging]
#plan32x4ne$plan
plan32x4se <- superimpose.plan(plan32x4.plan,
map.data=trimmed.dat,
start.point=c(224,3),
plot.dim=c(2,4),
row.buffer=1,sample.vgm=sample.vgm
)
## [using ordinary kriging]
#plan32x4se$plan